Overview

Dataset statistics

Number of variables15
Number of observations347
Missing cells12
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.8 KiB
Average record size in memory120.4 B

Variable types

Categorical2
Numeric13

Alerts

name has a high cardinality: 341 distinct valuesHigh cardinality
school_rating is highly overall correlated with reduced_lunch and 6 other fieldsHigh correlation
size is highly overall correlated with stu_teach_ratio and 1 other fieldsHigh correlation
reduced_lunch is highly overall correlated with school_rating and 6 other fieldsHigh correlation
state_percentile_16 is highly overall correlated with school_rating and 6 other fieldsHigh correlation
state_percentile_15 is highly overall correlated with school_rating and 6 other fieldsHigh correlation
stu_teach_ratio is highly overall correlated with size and 1 other fieldsHigh correlation
avg_score_15 is highly overall correlated with school_rating and 6 other fieldsHigh correlation
avg_score_16 is highly overall correlated with school_rating and 6 other fieldsHigh correlation
full_time_teachers is highly overall correlated with sizeHigh correlation
percent_black is highly overall correlated with school_rating and 6 other fieldsHigh correlation
percent_white is highly overall correlated with school_rating and 7 other fieldsHigh correlation
percent_hispanic is highly overall correlated with percent_whiteHigh correlation
school_type is highly overall correlated with stu_teach_ratioHigh correlation
school_type is highly imbalanced (62.7%)Imbalance
state_percentile_15 has 6 (1.7%) missing valuesMissing
avg_score_15 has 6 (1.7%) missing valuesMissing
name is uniformly distributedUniform
school_rating has 43 (12.4%) zerosZeros
percent_black has 4 (1.2%) zerosZeros
percent_asian has 23 (6.6%) zerosZeros

Reproduction

Analysis started2023-05-12 17:25:48.503507
Analysis finished2023-05-12 17:26:03.617999
Duration15.11 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct341
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Liberty Elementary
 
3
South Side Elementary
 
2
Johnson Elementary
 
2
Eakin Elementary
 
2
Rock Springs Elementary
 
2
Other values (336)
336 

Length

Max length49
Median length35
Mean length21.948127
Min length10

Characters and Unicode

Total characters7616
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)96.8%

Sample

1st rowAllendale Elementary School
2nd rowAnderson Elementary
3rd rowAvoca Elementary
4th rowBailey Middle
5th rowBarfield Elementary

Common Values

ValueCountFrequency (%)
Liberty Elementary 3
 
0.9%
South Side Elementary 2
 
0.6%
Johnson Elementary 2
 
0.6%
Eakin Elementary 2
 
0.6%
Rock Springs Elementary 2
 
0.6%
Allendale Elementary School 1
 
0.3%
Portland East Middle School 1
 
0.3%
Poplar Grove K-4 1
 
0.3%
Poplar Grove 5-8 1
 
0.3%
Pickett County High School 1
 
0.3%
Other values (331) 331
95.4%

Length

2023-05-12T20:26:03.684904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
elementary 197
 
19.1%
school 124
 
12.0%
middle 74
 
7.2%
high 55
 
5.3%
west 7
 
0.7%
creek 7
 
0.7%
j 7
 
0.7%
hill 6
 
0.6%
park 6
 
0.6%
john 6
 
0.6%
Other values (379) 545
52.7%

Most occurring characters

ValueCountFrequency (%)
e 854
 
11.2%
687
 
9.0%
l 629
 
8.3%
o 487
 
6.4%
a 467
 
6.1%
n 450
 
5.9%
r 438
 
5.8%
t 403
 
5.3%
i 338
 
4.4%
y 260
 
3.4%
Other values (45) 2603
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5842
76.7%
Uppercase Letter 1045
 
13.7%
Space Separator 687
 
9.0%
Other Punctuation 25
 
0.3%
Dash Punctuation 13
 
0.2%
Decimal Number 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 854
14.6%
l 629
10.8%
o 487
 
8.3%
a 467
 
8.0%
n 450
 
7.7%
r 438
 
7.5%
t 403
 
6.9%
i 338
 
5.8%
y 260
 
4.5%
d 253
 
4.3%
Other values (14) 1263
21.6%
Uppercase Letter
ValueCountFrequency (%)
E 219
21.0%
S 177
16.9%
M 110
10.5%
H 105
10.0%
C 59
 
5.6%
W 50
 
4.8%
B 41
 
3.9%
P 39
 
3.7%
L 29
 
2.8%
J 29
 
2.8%
Other values (12) 187
17.9%
Other Punctuation
ValueCountFrequency (%)
. 21
84.0%
' 2
 
8.0%
@ 1
 
4.0%
/ 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
8 2
50.0%
5 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
687
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6887
90.4%
Common 729
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 854
 
12.4%
l 629
 
9.1%
o 487
 
7.1%
a 467
 
6.8%
n 450
 
6.5%
r 438
 
6.4%
t 403
 
5.9%
i 338
 
4.9%
y 260
 
3.8%
d 253
 
3.7%
Other values (36) 2308
33.5%
Common
ValueCountFrequency (%)
687
94.2%
. 21
 
2.9%
- 13
 
1.8%
8 2
 
0.3%
' 2
 
0.3%
5 1
 
0.1%
4 1
 
0.1%
@ 1
 
0.1%
/ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 854
 
11.2%
687
 
9.0%
l 629
 
8.3%
o 487
 
6.4%
a 467
 
6.1%
n 450
 
5.9%
r 438
 
5.8%
t 403
 
5.3%
i 338
 
4.4%
y 260
 
3.4%
Other values (45) 2603
34.2%

school_rating
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9682997
Minimum0
Maximum5
Zeros43
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:03.781449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6903768
Coefficient of variation (CV)0.56947645
Kurtosis-1.0799062
Mean2.9682997
Median Absolute Deviation (MAD)1
Skewness-0.43390966
Sum1030
Variance2.8573737
MonotonicityNot monotonic
2023-05-12T20:26:03.862104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 86
24.8%
5 78
22.5%
3 56
16.1%
2 44
12.7%
0 43
12.4%
1 40
11.5%
ValueCountFrequency (%)
0 43
12.4%
1 40
11.5%
2 44
12.7%
3 56
16.1%
4 86
24.8%
5 78
22.5%
ValueCountFrequency (%)
5 78
22.5%
4 86
24.8%
3 56
16.1%
2 44
12.7%
1 40
11.5%
0 43
12.4%

size
Real number (ℝ)

Distinct297
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean699.47262
Minimum53
Maximum2314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:03.964900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile269.9
Q1420.5
median595
Q3851
95-th percentile1548.4
Maximum2314
Range2261
Interquartile range (IQR)430.5

Descriptive statistics

Standard deviation400.59864
Coefficient of variation (CV)0.57271525
Kurtosis2.6304011
Mean699.47262
Median Absolute Deviation (MAD)200
Skewness1.5434999
Sum242717
Variance160479.27
MonotonicityNot monotonic
2023-05-12T20:26:04.077826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851 3
 
0.9%
383 3
 
0.9%
510 3
 
0.9%
457 3
 
0.9%
611 3
 
0.9%
699 2
 
0.6%
866 2
 
0.6%
410 2
 
0.6%
657 2
 
0.6%
557 2
 
0.6%
Other values (287) 322
92.8%
ValueCountFrequency (%)
53 1
0.3%
71 1
0.3%
118 1
0.3%
122 1
0.3%
124 1
0.3%
141 1
0.3%
178 1
0.3%
181 1
0.3%
182 1
0.3%
190 1
0.3%
ValueCountFrequency (%)
2314 1
0.3%
2251 1
0.3%
2090 1
0.3%
2025 1
0.3%
2021 1
0.3%
1993 1
0.3%
1983 1
0.3%
1894 1
0.3%
1864 1
0.3%
1823 1
0.3%

reduced_lunch
Real number (ℝ)

Distinct91
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.279539
Minimum2
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:04.188735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q130
median51
Q371.5
95-th percentile89
Maximum98
Range96
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation25.480236
Coefficient of variation (CV)0.50677148
Kurtosis-0.97222709
Mean50.279539
Median Absolute Deviation (MAD)21
Skewness-0.11269656
Sum17447
Variance649.24244
MonotonicityNot monotonic
2023-05-12T20:26:04.300249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 9
 
2.6%
43 8
 
2.3%
65 8
 
2.3%
86 7
 
2.0%
55 7
 
2.0%
48 6
 
1.7%
66 6
 
1.7%
46 6
 
1.7%
87 6
 
1.7%
79 6
 
1.7%
Other values (81) 278
80.1%
ValueCountFrequency (%)
2 6
1.7%
3 3
0.9%
4 4
1.2%
5 2
 
0.6%
6 2
 
0.6%
7 3
0.9%
8 4
1.2%
9 3
0.9%
10 2
 
0.6%
12 2
 
0.6%
ValueCountFrequency (%)
98 2
 
0.6%
97 1
 
0.3%
94 1
 
0.3%
93 2
 
0.6%
92 1
 
0.3%
91 5
1.4%
90 4
1.2%
89 5
1.4%
88 3
0.9%
87 6
1.7%

state_percentile_16
Real number (ℝ)

Distinct311
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.801729
Minimum0.2
Maximum99.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:04.413228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile3.56
Q130.95
median66.4
Q388
95-th percentile98.14
Maximum99.8
Range99.6
Interquartile range (IQR)57.05

Descriptive statistics

Standard deviation32.540747
Coefficient of variation (CV)0.55339779
Kurtosis-1.2158246
Mean58.801729
Median Absolute Deviation (MAD)25.5
Skewness-0.43802388
Sum20404.2
Variance1058.9002
MonotonicityNot monotonic
2023-05-12T20:26:04.521663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.2 3
 
0.9%
4.3 2
 
0.6%
80.2 2
 
0.6%
82.2 2
 
0.6%
96.6 2
 
0.6%
83.6 2
 
0.6%
98.6 2
 
0.6%
93.7 2
 
0.6%
94.5 2
 
0.6%
93.1 2
 
0.6%
Other values (301) 326
93.9%
ValueCountFrequency (%)
0.2 1
0.3%
0.5 1
0.3%
0.6 1
0.3%
0.7 1
0.3%
0.8 1
0.3%
1.1 1
0.3%
1.3 1
0.3%
1.5 1
0.3%
1.6 1
0.3%
1.8 1
0.3%
ValueCountFrequency (%)
99.8 1
0.3%
99.6 1
0.3%
99.5 1
0.3%
99.4 1
0.3%
99.3 1
0.3%
99.2 1
0.3%
99.1 1
0.3%
98.9 2
0.6%
98.8 1
0.3%
98.7 1
0.3%

state_percentile_15
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct298
Distinct (%)87.4%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean58.249267
Minimum0.6
Maximum99.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:04.629683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile3.7
Q127.1
median65.8
Q388.6
95-th percentile98
Maximum99.8
Range99.2
Interquartile range (IQR)61.5

Descriptive statistics

Standard deviation32.70263
Coefficient of variation (CV)0.5614256
Kurtosis-1.3208329
Mean58.249267
Median Absolute Deviation (MAD)26.8
Skewness-0.37138574
Sum19863
Variance1069.462
MonotonicityNot monotonic
2023-05-12T20:26:04.736601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.6 3
 
0.9%
77.9 3
 
0.9%
97.7 3
 
0.9%
64.2 2
 
0.6%
90 2
 
0.6%
26.8 2
 
0.6%
98.4 2
 
0.6%
91.6 2
 
0.6%
89 2
 
0.6%
25.6 2
 
0.6%
Other values (288) 318
91.6%
(Missing) 6
 
1.7%
ValueCountFrequency (%)
0.6 1
0.3%
0.7 1
0.3%
0.8 1
0.3%
1 2
0.6%
1.1 1
0.3%
1.2 1
0.3%
1.9 1
0.3%
2.3 2
0.6%
2.5 1
0.3%
2.6 1
0.3%
ValueCountFrequency (%)
99.8 1
0.3%
99.7 1
0.3%
99.6 1
0.3%
99.5 1
0.3%
99.4 1
0.3%
99.3 1
0.3%
99.2 1
0.3%
99 1
0.3%
98.8 1
0.3%
98.7 1
0.3%

stu_teach_ratio
Real number (ℝ)

Distinct101
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.461671
Minimum4.7
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:04.844961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile11.3
Q113.7
median15
Q316.7
95-th percentile19.2
Maximum111
Range106.3
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.7251701
Coefficient of variation (CV)0.37028145
Kurtosis225.30336
Mean15.461671
Median Absolute Deviation (MAD)1.5
Skewness13.531794
Sum5365.2
Variance32.777573
MonotonicityNot monotonic
2023-05-12T20:26:04.954231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.5 13
 
3.7%
14.8 11
 
3.2%
13.6 10
 
2.9%
14.4 9
 
2.6%
14.7 8
 
2.3%
13.8 8
 
2.3%
14.5 7
 
2.0%
15 7
 
2.0%
16.8 7
 
2.0%
14.1 7
 
2.0%
Other values (91) 260
74.9%
ValueCountFrequency (%)
4.7 1
0.3%
7.3 1
0.3%
9.5 1
0.3%
9.8 1
0.3%
10 1
0.3%
10.1 1
0.3%
10.3 2
0.6%
10.7 2
0.6%
10.8 1
0.3%
10.9 1
0.3%
ValueCountFrequency (%)
111 1
0.3%
31 1
0.3%
22.5 1
0.3%
22.4 1
0.3%
22.3 1
0.3%
21.1 1
0.3%
20.7 1
0.3%
20.5 1
0.3%
20.3 1
0.3%
20 1
0.3%

school_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Public
292 
Public Magnet
46 
Public Charter
 
8
Public Virtual
 
1

Length

Max length14
Median length6
Mean length7.1354467
Min length6

Characters and Unicode

Total characters2476
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowPublic
2nd rowPublic
3rd rowPublic
4th rowPublic Magnet
5th rowPublic

Common Values

ValueCountFrequency (%)
Public 292
84.1%
Public Magnet 46
 
13.3%
Public Charter 8
 
2.3%
Public Virtual 1
 
0.3%

Length

2023-05-12T20:26:05.054918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T20:26:05.157397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
public 347
86.3%
magnet 46
 
11.4%
charter 8
 
2.0%
virtual 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u 348
14.1%
l 348
14.1%
i 348
14.1%
P 347
14.0%
b 347
14.0%
c 347
14.0%
t 55
 
2.2%
a 55
 
2.2%
55
 
2.2%
e 54
 
2.2%
Other values (7) 172
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2019
81.5%
Uppercase Letter 402
 
16.2%
Space Separator 55
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 348
17.2%
l 348
17.2%
i 348
17.2%
b 347
17.2%
c 347
17.2%
t 55
 
2.7%
a 55
 
2.7%
e 54
 
2.7%
g 46
 
2.3%
n 46
 
2.3%
Other values (2) 25
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
P 347
86.3%
M 46
 
11.4%
C 8
 
2.0%
V 1
 
0.2%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2421
97.8%
Common 55
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 348
14.4%
l 348
14.4%
i 348
14.4%
P 347
14.3%
b 347
14.3%
c 347
14.3%
t 55
 
2.3%
a 55
 
2.3%
e 54
 
2.2%
M 46
 
1.9%
Other values (6) 126
 
5.2%
Common
ValueCountFrequency (%)
55
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 348
14.1%
l 348
14.1%
i 348
14.1%
P 347
14.0%
b 347
14.0%
c 347
14.0%
t 55
 
2.2%
a 55
 
2.2%
55
 
2.2%
e 54
 
2.2%
Other values (7) 172
6.9%

avg_score_15
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct282
Distinct (%)82.7%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean57.004692
Minimum1.5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:05.343535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile7.9
Q137.6
median61.8
Q379.6
95-th percentile93.6
Maximum99
Range97.5
Interquartile range (IQR)42

Descriptive statistics

Standard deviation26.69645
Coefficient of variation (CV)0.46832022
Kurtosis-0.93407581
Mean57.004692
Median Absolute Deviation (MAD)20.5
Skewness-0.42766295
Sum19438.6
Variance712.70045
MonotonicityNot monotonic
2023-05-12T20:26:05.452429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.4 3
 
0.9%
60.8 3
 
0.9%
73.1 3
 
0.9%
69.4 3
 
0.9%
84.3 3
 
0.9%
80.6 3
 
0.9%
49 3
 
0.9%
61.8 2
 
0.6%
89.6 2
 
0.6%
11.3 2
 
0.6%
Other values (272) 314
90.5%
(Missing) 6
 
1.7%
ValueCountFrequency (%)
1.5 1
0.3%
1.7 2
0.6%
2.1 1
0.3%
2.4 1
0.3%
2.5 1
0.3%
2.7 1
0.3%
4.3 1
0.3%
4.4 1
0.3%
5.2 1
0.3%
5.3 1
0.3%
ValueCountFrequency (%)
99 1
0.3%
96 1
0.3%
95.3 2
0.6%
95.2 1
0.3%
94.9 2
0.6%
94.5 1
0.3%
94.4 1
0.3%
94.1 1
0.3%
94 2
0.6%
93.9 1
0.3%

avg_score_16
Real number (ℝ)

Distinct288
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.049856
Minimum0.1
Maximum98.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:05.564355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile7.57
Q137
median60.7
Q380.25
95-th percentile95.27
Maximum98.9
Range98.8
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation27.968974
Coefficient of variation (CV)0.49025494
Kurtosis-0.9962468
Mean57.049856
Median Absolute Deviation (MAD)22
Skewness-0.40601311
Sum19796.3
Variance782.26349
MonotonicityNot monotonic
2023-05-12T20:26:05.675538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.5 4
 
1.2%
70.3 3
 
0.9%
73.1 3
 
0.9%
50.5 3
 
0.9%
47.7 3
 
0.9%
49.8 2
 
0.6%
39.2 2
 
0.6%
57.8 2
 
0.6%
77.8 2
 
0.6%
75 2
 
0.6%
Other values (278) 321
92.5%
ValueCountFrequency (%)
0.1 1
0.3%
1.9 1
0.3%
2.3 1
0.3%
2.4 1
0.3%
2.8 2
0.6%
4.4 1
0.3%
4.5 1
0.3%
4.8 1
0.3%
4.9 1
0.3%
5.8 1
0.3%
ValueCountFrequency (%)
98.9 1
0.3%
98.5 1
0.3%
98.2 1
0.3%
97.9 1
0.3%
97.7 1
0.3%
97.5 2
0.6%
97.4 1
0.3%
97 1
0.3%
96.9 1
0.3%
96.6 1
0.3%

full_time_teachers
Real number (ℝ)

Distinct86
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.939481
Minimum2
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:05.783959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18.3
Q130
median40
Q354
95-th percentile92
Maximum140
Range138
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.053386
Coefficient of variation (CV)0.49073521
Kurtosis2.8676445
Mean44.939481
Median Absolute Deviation (MAD)12
Skewness1.4284454
Sum15594
Variance486.35182
MonotonicityNot monotonic
2023-05-12T20:26:05.886544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 15
 
4.3%
26 13
 
3.7%
54 12
 
3.5%
42 11
 
3.2%
37 10
 
2.9%
33 10
 
2.9%
27 10
 
2.9%
39 10
 
2.9%
35 9
 
2.6%
36 9
 
2.6%
Other values (76) 238
68.6%
ValueCountFrequency (%)
2 1
0.3%
3 1
0.3%
4 1
0.3%
6 1
0.3%
12 1
0.3%
13 1
0.3%
14 1
0.3%
15 2
0.6%
16 2
0.6%
17 2
0.6%
ValueCountFrequency (%)
140 1
 
0.3%
133 1
 
0.3%
132 1
 
0.3%
116 4
1.2%
115 1
 
0.3%
105 1
 
0.3%
103 1
 
0.3%
102 1
 
0.3%
100 1
 
0.3%
98 1
 
0.3%

percent_black
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct230
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.197983
Minimum0
Maximum97.4
Zeros4
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:06.003732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q13.6
median13.5
Q328.35
95-th percentile80.91
Maximum97.4
Range97.4
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation23.562538
Coefficient of variation (CV)1.1115462
Kurtosis2.0384927
Mean21.197983
Median Absolute Deviation (MAD)10.8
Skewness1.6246382
Sum7355.7
Variance555.1932
MonotonicityNot monotonic
2023-05-12T20:26:06.111535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 9
 
2.6%
1.4 5
 
1.4%
3.6 4
 
1.2%
1.1 4
 
1.2%
4.9 4
 
1.2%
0 4
 
1.2%
2.5 4
 
1.2%
0.7 4
 
1.2%
18.6 3
 
0.9%
3.2 3
 
0.9%
Other values (220) 303
87.3%
ValueCountFrequency (%)
0 4
1.2%
0.1 1
 
0.3%
0.2 1
 
0.3%
0.3 3
 
0.9%
0.4 2
 
0.6%
0.5 3
 
0.9%
0.6 3
 
0.9%
0.7 4
1.2%
0.8 9
2.6%
0.9 3
 
0.9%
ValueCountFrequency (%)
97.4 1
0.3%
95.1 1
0.3%
94.6 1
0.3%
93.4 1
0.3%
92.5 1
0.3%
92 1
0.3%
91.7 1
0.3%
91.3 1
0.3%
89.7 1
0.3%
89.3 1
0.3%

percent_white
Real number (ℝ)

Distinct269
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.673487
Minimum1.1
Maximum99.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:06.222107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile8.73
Q140.6
median68.7
Q385.95
95-th percentile94.17
Maximum99.7
Range98.6
Interquartile range (IQR)45.35

Descriptive statistics

Standard deviation27.274859
Coefficient of variation (CV)0.44224609
Kurtosis-0.76210171
Mean61.673487
Median Absolute Deviation (MAD)18.7
Skewness-0.63631169
Sum21400.7
Variance743.91791
MonotonicityNot monotonic
2023-05-12T20:26:06.330166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.3 4
 
1.2%
77.4 4
 
1.2%
86.9 4
 
1.2%
83.3 3
 
0.9%
88.7 3
 
0.9%
83.5 3
 
0.9%
86.8 3
 
0.9%
90.5 3
 
0.9%
87.4 3
 
0.9%
91.4 3
 
0.9%
Other values (259) 314
90.5%
ValueCountFrequency (%)
1.1 2
0.6%
2.2 1
0.3%
2.6 1
0.3%
2.7 1
0.3%
3.5 1
0.3%
3.6 1
0.3%
3.7 1
0.3%
4.2 1
0.3%
4.9 1
0.3%
5.1 1
0.3%
ValueCountFrequency (%)
99.7 1
 
0.3%
97.6 1
 
0.3%
97.5 1
 
0.3%
96.9 1
 
0.3%
96.7 2
0.6%
96.5 2
0.6%
96.3 2
0.6%
96 1
 
0.3%
95.9 3
0.9%
95.4 1
 
0.3%

percent_asian
Real number (ℝ)

Distinct84
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6426513
Minimum0
Maximum21.1
Zeros23
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:06.438853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.6
Q33.1
95-th percentile9.64
Maximum21.1
Range21.1
Interquartile range (IQR)2.35

Descriptive statistics

Standard deviation3.1096293
Coefficient of variation (CV)1.1767081
Kurtosis8.0502235
Mean2.6426513
Median Absolute Deviation (MAD)1
Skewness2.512201
Sum917
Variance9.6697941
MonotonicityNot monotonic
2023-05-12T20:26:06.541867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
6.6%
0.3 16
 
4.6%
0.7 14
 
4.0%
0.9 13
 
3.7%
1.4 13
 
3.7%
1.6 11
 
3.2%
1.2 11
 
3.2%
0.6 11
 
3.2%
1.8 11
 
3.2%
1 10
 
2.9%
Other values (74) 214
61.7%
ValueCountFrequency (%)
0 23
6.6%
0.1 3
 
0.9%
0.2 4
 
1.2%
0.3 16
4.6%
0.4 9
 
2.6%
0.5 7
 
2.0%
0.6 11
3.2%
0.7 14
4.0%
0.8 7
 
2.0%
0.9 13
3.7%
ValueCountFrequency (%)
21.1 1
0.3%
19.5 1
0.3%
17 1
0.3%
14.5 1
0.3%
12.7 1
0.3%
12.5 1
0.3%
12.1 1
0.3%
11.8 2
0.6%
11.4 1
0.3%
11 1
0.3%

percent_hispanic
Real number (ℝ)

Distinct180
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.164553
Minimum0
Maximum65.2
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-05-12T20:26:06.645241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.63
Q13.8
median6.4
Q313.8
95-th percentile37.35
Maximum65.2
Range65.2
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.030608
Coefficient of variation (CV)1.0775718
Kurtosis5.2185039
Mean11.164553
Median Absolute Deviation (MAD)3.6
Skewness2.2157133
Sum3874.1
Variance144.73553
MonotonicityNot monotonic
2023-05-12T20:26:06.748161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 8
 
2.3%
3 8
 
2.3%
2.1 7
 
2.0%
4.6 7
 
2.0%
4.2 6
 
1.7%
2.8 6
 
1.7%
2.6 6
 
1.7%
2.9 5
 
1.4%
3.3 5
 
1.4%
4.5 5
 
1.4%
Other values (170) 284
81.8%
ValueCountFrequency (%)
0 2
0.6%
0.7 1
 
0.3%
0.9 1
 
0.3%
1 2
0.6%
1.1 3
0.9%
1.3 3
0.9%
1.4 3
0.9%
1.5 1
 
0.3%
1.6 2
0.6%
1.7 2
0.6%
ValueCountFrequency (%)
65.2 1
0.3%
64.8 1
0.3%
61.3 1
0.3%
61.2 1
0.3%
56.1 1
0.3%
52.5 1
0.3%
50.9 1
0.3%
50 1
0.3%
49.6 1
0.3%
49.5 1
0.3%

Interactions

2023-05-12T20:26:02.007362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.327945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.460673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.480781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.457625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.515636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.538190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.558263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.694527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.721213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.796694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.957067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.955203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.090900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.428656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.536671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.560578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.530622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.594947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.614356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.636139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.771335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.804814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.877321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.033740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.031273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.172885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.504569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.608112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.633911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.600279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.667944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.688816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.709208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.848886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.888707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.957605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.104661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.102118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.259409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.582987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.687876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.706154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.673894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.744287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.765454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.787198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.933702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.978643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.036677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.180228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.178644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.341636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.664314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.765367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.779818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.746361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.823614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.847535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.867761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.014871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.060332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.115495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.256630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.256522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.423031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.742606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.846377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.856130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.819520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.908000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.930459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.954172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.090640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.142302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.199763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.333793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.338042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.503078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.824732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.929434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.935276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.896747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.987014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.011369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.031622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.168083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.223634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.286139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.412691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.417565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.586224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:49.910738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.008976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.010844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.971079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.065081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.085572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.112713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.243854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.305696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.375509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.487717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.508748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.672509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.057092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.085690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.082510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.122105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.141360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.163655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.191481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.321425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.386491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.464043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.563975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.591151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.762653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.142569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.169969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.161647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.205995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.226481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.247802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.278723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.410914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.473247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.554354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.649624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.683282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.845042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.222370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.245322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.235262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.283578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.304143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.326492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.355598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.489527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.553685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.632878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.725532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.761782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:02.925880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.302066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.323331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.310679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.360860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.382752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.403162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.538975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.564921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.635496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.711637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.802206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.843132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:03.005576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:50.378833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:51.395481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:52.380931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:53.435526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:54.456572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:55.477916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:56.612831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:57.640767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:58.711779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:25:59.788132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:00.875268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T20:26:01.921069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-12T20:26:06.850286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
school_ratingsizereduced_lunchstate_percentile_16state_percentile_15stu_teach_ratioavg_score_15avg_score_16full_time_teacherspercent_blackpercent_whitepercent_asianpercent_hispanicschool_type
school_rating1.0000.210-0.8220.9820.9290.3410.9300.9780.132-0.5170.5360.313-0.3070.210
size0.2101.000-0.3200.2100.2010.5140.1950.1890.956-0.0470.0480.3450.1030.132
reduced_lunch-0.822-0.3201.000-0.830-0.843-0.383-0.844-0.822-0.2460.561-0.612-0.3840.4300.136
state_percentile_160.9820.210-0.8301.0000.9420.3400.9440.9970.132-0.5060.5270.324-0.3120.194
state_percentile_150.9290.201-0.8430.9421.0000.3420.9980.9430.122-0.5140.5210.364-0.2980.183
stu_teach_ratio0.3410.514-0.3830.3400.3421.0000.3420.3200.278-0.2010.2030.348-0.1250.575
avg_score_150.9300.195-0.8440.9440.9980.3421.0000.9470.116-0.5170.5240.367-0.2990.192
avg_score_160.9780.189-0.8220.9970.9430.3200.9471.0000.111-0.5090.5250.326-0.3030.183
full_time_teachers0.1320.956-0.2460.1320.1220.2780.1160.1111.0000.016-0.0180.2770.1570.214
percent_black-0.517-0.0470.561-0.506-0.514-0.201-0.517-0.5090.0161.000-0.9140.0600.4640.386
percent_white0.5360.048-0.6120.5270.5210.2030.5240.525-0.018-0.9141.000-0.150-0.6270.299
percent_asian0.3130.345-0.3840.3240.3640.3480.3670.3260.2770.060-0.1501.0000.1610.000
percent_hispanic-0.3070.1030.430-0.312-0.298-0.125-0.299-0.3030.1570.464-0.6270.1611.0000.189
school_type0.2100.1320.1360.1940.1830.5750.1920.1830.2140.3860.2990.0000.1891.000

Missing values

2023-05-12T20:26:03.232307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-12T20:26:03.434067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-12T20:26:03.564784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameschool_ratingsizereduced_lunchstate_percentile_16state_percentile_15stu_teach_ratioschool_typeavg_score_15avg_score_16full_time_teacherspercent_blackpercent_whitepercent_asianpercent_hispanic
0Allendale Elementary School5.0851.010.090.295.815.7Public89.485.254.02.985.51.65.6
1Anderson Elementary2.0412.071.032.837.312.8Public43.038.332.03.986.71.04.9
2Avoca Elementary4.0482.043.078.483.616.6Public75.773.029.01.091.51.24.4
3Bailey Middle0.0394.091.01.61.013.1Public Magnet2.14.430.080.711.72.34.3
4Barfield Elementary4.0948.026.085.389.214.8Public81.379.664.011.871.27.16.0
5Barkers Mill Elementary School4.0893.048.078.176.413.9Public69.472.364.028.639.92.217.8
6Barksdale Elementary4.0580.058.083.274.613.8Public68.076.142.027.459.50.56.6
7Beech Elementary5.0612.016.095.293.415.6Public85.990.339.04.290.50.33.1
8Beech Senior High School4.01274.021.082.577.914.9Public67.269.385.013.879.71.43.7
9Bellevue Middle3.0680.050.053.055.516.5Public Magnet55.853.341.024.361.85.66.6
nameschool_ratingsizereduced_lunchstate_percentile_16state_percentile_15stu_teach_ratioschool_typeavg_score_15avg_score_16full_time_teacherspercent_blackpercent_whitepercent_asianpercent_hispanic
337Whitthorne Middle School1.0948.065.026.326.817.2Public37.035.055.022.461.71.714.1
338Whitworth-Buchanan Middle School3.0851.060.050.453.714.6Public53.851.458.023.454.65.612.7
339William Henry Oliver Middle2.0739.057.045.549.616.7Public51.847.944.026.951.311.010.1
340Wilson Central High School3.01823.024.055.763.217.8Public60.851.9102.02.584.91.84.2
341Wilson Elementary School4.0800.025.084.689.014.5Public81.379.355.05.586.42.64.0
342Winfree Bryant Middle School3.0611.057.059.165.216.9Public61.457.736.015.266.31.515.7
343Winstead Elementary School5.0515.08.093.997.014.3Public92.089.336.03.387.43.14.1
344Woodland Elementary4.0424.055.084.876.714.1Public69.479.430.011.670.52.19.7
345Woodland Middle School5.0866.02.093.397.119.2Public89.884.945.04.577.610.04.4
346Wright Middle0.0829.089.04.51.216.5Public2.79.650.022.321.06.649.5